Papers › A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
Miquel Alfaras, Miguel C. Soriano, Silvia Ortín
We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection approaches.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Heartbeat Classification | AHA | ESN Ensembles (A Lead) | Accuracy (VEB+) | 98.6% | #1 of 1 | Archive leaderboard | report |
| Heartbeat Classification | AHA | ESN Ensembles (A Lead) | PPV (VEB+) | 94.9% | #1 of 1 | Archive leaderboard | report |
| Heartbeat Classification | AHA | ESN Ensembles (A Lead) | Sensitivity (VEB+) | 90.4% | #1 of 1 | Archive leaderboard | report |
| Heartbeat Classification | AHA | ESN Ensembles (A Lead) | Specificity (VEB+) | 99.5% | #1 of 1 | Archive leaderboard | report |
| Heartbeat Classification | MIT-BIH AR | ESN Ensembles (II Leads) | PPV (VEB) | 95.7% | #1 of 2 | Archive leaderboard | report |
| Heartbeat Classification | MIT-BIH AR | ESN Ensembles (II Leads) | Sensitivity (VEB) | 92.7% | #1 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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